Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations360336
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.8 MiB
Average record size in memory159.5 B

Variable types

Numeric8
Categorical8

Alerts

coffee_bar is highly overall correlated with florist and 2 other fieldsHigh correlation
florist is highly overall correlated with coffee_bar and 4 other fieldsHigh correlation
prepared_food is highly overall correlated with florist and 3 other fieldsHigh correlation
salad_bar is highly overall correlated with florist and 3 other fieldsHigh correlation
store_sqft is highly overall correlated with coffee_bar and 4 other fieldsHigh correlation
video_store is highly overall correlated with coffee_bar and 4 other fieldsHigh correlation
total_children has 36441 (10.1%) zerosZeros
num_children_at_home has 243555 (67.6%) zerosZeros

Reproduction

Analysis started2025-11-14 09:28:02.382032
Analysis finished2025-11-14 09:28:13.260642
Duration10.88 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

store_sales(in millions)
Real number (ℝ)

Distinct1044
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3373763
Minimum0.51
Maximum22.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:13.298620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile1.78
Q13.72
median5.78
Q38.4
95-th percentile11.91
Maximum22.92
Range22.41
Interquartile range (IQR)4.68

Descriptive statistics

Standard deviation3.3079796
Coefficient of variation (CV)0.52197936
Kurtosis0.075175055
Mean6.3373763
Median Absolute Deviation (MAD)2.26
Skewness0.66149169
Sum2283584.8
Variance10.942729
MonotonicityNot monotonic
2025-11-14T14:28:13.359548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.042373
 
0.7%
5.41988
 
0.6%
7.411919
 
0.5%
7.951874
 
0.5%
5.521831
 
0.5%
4.81726
 
0.5%
6.841716
 
0.5%
2.281676
 
0.5%
3.61651
 
0.5%
6.721569
 
0.4%
Other values (1034)342013
94.9%
ValueCountFrequency (%)
0.514
 
< 0.1%
0.5228
< 0.1%
0.5320
< 0.1%
0.546
 
< 0.1%
0.5513
< 0.1%
0.5614
< 0.1%
0.5724
< 0.1%
0.5820
< 0.1%
0.628
< 0.1%
0.6117
< 0.1%
ValueCountFrequency (%)
22.921
 
< 0.1%
22.161
 
< 0.1%
20.61
 
< 0.1%
20.141
 
< 0.1%
19.951
 
< 0.1%
19.918
 
< 0.1%
19.859
 
< 0.1%
19.88
 
< 0.1%
19.7559
< 0.1%
19.714
 
< 0.1%

unit_sales(in millions)
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0438813
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:13.626231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78467595
Coefficient of variation (CV)0.25778796
Kurtosis-0.25012041
Mean3.0438813
Median Absolute Deviation (MAD)1
Skewness-0.10799835
Sum1096820
Variance0.61571635
MonotonicityNot monotonic
2025-11-14T14:28:13.663401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3175556
48.7%
494999
26.4%
277240
21.4%
16765
 
1.9%
55745
 
1.6%
631
 
< 0.1%
ValueCountFrequency (%)
16765
 
1.9%
277240
21.4%
3175556
48.7%
494999
26.4%
55745
 
1.6%
631
 
< 0.1%
ValueCountFrequency (%)
631
 
< 0.1%
55745
 
1.6%
494999
26.4%
3175556
48.7%
277240
21.4%
16765
 
1.9%

total_children
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4564823
Minimum0
Maximum5
Zeros36441
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:13.698680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.4889919
Coefficient of variation (CV)0.60614803
Kurtosis-1.0394866
Mean2.4564823
Median Absolute Deviation (MAD)1
Skewness0.035134467
Sum885159
Variance2.2170969
MonotonicityNot monotonic
2025-11-14T14:28:13.734907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
174857
20.8%
273902
20.5%
371524
19.8%
470134
19.5%
036441
10.1%
533478
9.3%
ValueCountFrequency (%)
036441
10.1%
174857
20.8%
273902
20.5%
371524
19.8%
470134
19.5%
533478
9.3%
ValueCountFrequency (%)
533478
9.3%
470134
19.5%
371524
19.8%
273902
20.5%
174857
20.8%
036441
10.1%

num_children_at_home
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6893899
Minimum0
Maximum5
Zeros243555
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:13.769857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2147324
Coefficient of variation (CV)1.7620398
Kurtosis2.5983024
Mean0.6893899
Median Absolute Deviation (MAD)0
Skewness1.8486807
Sum248412
Variance1.4755749
MonotonicityNot monotonic
2025-11-14T14:28:13.803959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0243555
67.6%
149427
 
13.7%
228163
 
7.8%
320382
 
5.7%
412532
 
3.5%
56277
 
1.7%
ValueCountFrequency (%)
0243555
67.6%
149427
 
13.7%
228163
 
7.8%
320382
 
5.7%
412532
 
3.5%
56277
 
1.7%
ValueCountFrequency (%)
56277
 
1.7%
412532
 
3.5%
320382
 
5.7%
228163
 
7.8%
149427
 
13.7%
0243555
67.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
2.0
110401 
3.0
104424 
1.0
82619 
4.0
44355 
0.0
18537 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row0.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0110401
30.6%
3.0104424
29.0%
1.082619
22.9%
4.044355
12.3%
0.018537
 
5.1%

Length

2025-11-14T14:28:13.849573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:13.885030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0110401
30.6%
3.0104424
29.0%
1.082619
22.9%
4.044355
12.3%
0.018537
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0378873
35.0%
.360336
33.3%
2110401
 
10.2%
3104424
 
9.7%
182619
 
7.6%
444355
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0378873
35.0%
.360336
33.3%
2110401
 
10.2%
3104424
 
9.7%
182619
 
7.6%
444355
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0378873
35.0%
.360336
33.3%
2110401
 
10.2%
3104424
 
9.7%
182619
 
7.6%
444355
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0378873
35.0%
.360336
33.3%
2110401
 
10.2%
3104424
 
9.7%
182619
 
7.6%
444355
 
4.1%

gross_weight
Real number (ℝ)

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.822071
Minimum6
Maximum21.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:13.934655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.98
Q19.71
median13.6
Q317.7
95-th percentile21.2
Maximum21.9
Range15.9
Interquartile range (IQR)7.99

Descriptive statistics

Standard deviation4.6147921
Coefficient of variation (CV)0.33387125
Kurtosis-1.2321255
Mean13.822071
Median Absolute Deviation (MAD)4
Skewness0.093304798
Sum4980589.8
Variance21.296306
MonotonicityNot monotonic
2025-11-14T14:28:13.992584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.14372
 
1.2%
14.73906
 
1.1%
19.93800
 
1.1%
17.23616
 
1.0%
13.73516
 
1.0%
13.23431
 
1.0%
18.73430
 
1.0%
16.13397
 
0.9%
20.93374
 
0.9%
21.83248
 
0.9%
Other values (374)324246
90.0%
ValueCountFrequency (%)
6210
0.1%
6.03157
 
< 0.1%
6.04166
 
< 0.1%
6.06358
0.1%
6.09198
0.1%
6.11415
0.1%
6.12226
0.1%
6.13197
0.1%
6.14395
0.1%
6.15207
0.1%
ValueCountFrequency (%)
21.93106
0.9%
21.83248
0.9%
21.73130
0.9%
21.61724
0.5%
21.51926
0.5%
21.41868
0.5%
21.32721
0.8%
21.22837
0.8%
21.11556
0.4%
212537
0.7%

recyclable_package
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1.0
204702 
0.0
155634 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0204702
56.8%
0.0155634
43.2%

Length

2025-11-14T14:28:14.049553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.078905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0204702
56.8%
0.0155634
43.2%

Most occurring characters

ValueCountFrequency (%)
0515970
47.7%
.360336
33.3%
1204702
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0515970
47.7%
.360336
33.3%
1204702
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0515970
47.7%
.360336
33.3%
1204702
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0515970
47.7%
.360336
33.3%
1204702
 
18.9%

low_fat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
0.0
242219 
1.0
118117 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0242219
67.2%
1.0118117
32.8%

Length

2025-11-14T14:28:14.119982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.152342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0242219
67.2%
1.0118117
32.8%

Most occurring characters

ValueCountFrequency (%)
0602555
55.7%
.360336
33.3%
1118117
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0602555
55.7%
.360336
33.3%
1118117
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0602555
55.7%
.360336
33.3%
1118117
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0602555
55.7%
.360336
33.3%
1118117
 
10.9%

units_per_case
Real number (ℝ)

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.972706
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:14.188616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median20
Q328
95-th percentile34
Maximum36
Range35
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.212912
Coefficient of variation (CV)0.53829498
Kurtosis-1.2445497
Mean18.972706
Median Absolute Deviation (MAD)9
Skewness-0.10200888
Sum6836549
Variance104.30358
MonotonicityNot monotonic
2025-11-14T14:28:14.240201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2914626
 
4.1%
614064
 
3.9%
3313483
 
3.7%
3112947
 
3.6%
3012847
 
3.6%
2312517
 
3.5%
2612050
 
3.3%
2511651
 
3.2%
511624
 
3.2%
911219
 
3.1%
Other values (26)233308
64.7%
ValueCountFrequency (%)
15273
 
1.5%
29195
2.6%
310736
3.0%
49194
2.6%
511624
3.2%
614064
3.9%
78367
2.3%
87928
2.2%
911219
3.1%
108217
2.3%
ValueCountFrequency (%)
364005
 
1.1%
359203
2.6%
3410401
2.9%
3313483
3.7%
329668
2.7%
3112947
3.6%
3012847
3.6%
2914626
4.1%
288960
2.5%
279697
2.7%

store_sqft
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28180.333
Minimum20319
Maximum39696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:14.286439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20319
5-th percentile20319
Q123593
median27694
Q333858
95-th percentile39696
Maximum39696
Range19377
Interquartile range (IQR)10265

Descriptive statistics

Standard deviation5968.8741
Coefficient of variation (CV)0.21180992
Kurtosis-1.0730401
Mean28180.333
Median Absolute Deviation (MAD)4582
Skewness0.3926991
Sum1.0154389 × 1010
Variance35627458
MonotonicityNot monotonic
2025-11-14T14:28:14.335740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2121531807
 
8.8%
2769430280
 
8.4%
3385830218
 
8.4%
2359829659
 
8.2%
2031929193
 
8.1%
3026825995
 
7.2%
3079724271
 
6.7%
3969619690
 
5.5%
2311217765
 
4.9%
3479116498
 
4.6%
Other values (10)104960
29.1%
ValueCountFrequency (%)
2031929193
8.1%
2121531807
8.8%
224787314
 
2.0%
2311217765
4.9%
2359311877
 
3.3%
2359829659
8.2%
2368816072
4.5%
2375914934
4.1%
245973486
 
1.0%
2769430280
8.4%
ValueCountFrequency (%)
3969619690
5.5%
3838216454
4.6%
365099701
 
2.7%
3479116498
4.6%
344525319
 
1.5%
3385830218
8.4%
3079724271
6.7%
3058413016
3.6%
3026825995
7.2%
282066787
 
1.9%

coffee_bar
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1.0
203532 
0.0
156804 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0203532
56.5%
0.0156804
43.5%

Length

2025-11-14T14:28:14.383394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.412773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0203532
56.5%
0.0156804
43.5%

Most occurring characters

ValueCountFrequency (%)
0517140
47.8%
.360336
33.3%
1203532
 
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0517140
47.8%
.360336
33.3%
1203532
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0517140
47.8%
.360336
33.3%
1203532
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0517140
47.8%
.360336
33.3%
1203532
 
18.8%

video_store
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
0.0
260381 
1.0
99955 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0260381
72.3%
1.099955
 
27.7%

Length

2025-11-14T14:28:14.449858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.479747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0260381
72.3%
1.099955
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0620717
57.4%
.360336
33.3%
199955
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0620717
57.4%
.360336
33.3%
199955
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0620717
57.4%
.360336
33.3%
199955
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0620717
57.4%
.360336
33.3%
199955
 
9.2%

salad_bar
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1.0
181900 
0.0
178436 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0181900
50.5%
0.0178436
49.5%

Length

2025-11-14T14:28:14.515758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.546895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0181900
50.5%
0.0178436
49.5%

Most occurring characters

ValueCountFrequency (%)
0538772
49.8%
.360336
33.3%
1181900
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0538772
49.8%
.360336
33.3%
1181900
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0538772
49.8%
.360336
33.3%
1181900
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0538772
49.8%
.360336
33.3%
1181900
 
16.8%

prepared_food
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1.0
181909 
0.0
178427 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0181909
50.5%
0.0178427
49.5%

Length

2025-11-14T14:28:14.584668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.613534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0181909
50.5%
0.0178427
49.5%

Most occurring characters

ValueCountFrequency (%)
0538763
49.8%
.360336
33.3%
1181909
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0538763
49.8%
.360336
33.3%
1181909
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0538763
49.8%
.360336
33.3%
1181909
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0538763
49.8%
.360336
33.3%
1181909
 
16.8%

florist
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.6 MiB
1.0
181318 
0.0
179018 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1081008
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0181318
50.3%
0.0179018
49.7%

Length

2025-11-14T14:28:14.649169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T14:28:14.677961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0181318
50.3%
0.0179018
49.7%

Most occurring characters

ValueCountFrequency (%)
0539354
49.9%
.360336
33.3%
1181318
 
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0539354
49.9%
.360336
33.3%
1181318
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0539354
49.9%
.360336
33.3%
1181318
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1081008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0539354
49.9%
.360336
33.3%
1181318
 
16.8%

cost
Real number (ℝ)

Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.614729
Minimum50.79
Maximum149.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.6 MiB
2025-11-14T14:28:14.716904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.79
5-th percentile53.82
Q170.32
median98.81
Q3126.62
95-th percentile145.41
Maximum149.75
Range98.96
Interquartile range (IQR)56.3

Descriptive statistics

Standard deviation29.939435
Coefficient of variation (CV)0.30055229
Kurtosis-1.263302
Mean99.614729
Median Absolute Deviation (MAD)27.81
Skewness0.019132207
Sum35894773
Variance896.36974
MonotonicityNot monotonic
2025-11-14T14:28:14.772172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101.844766
 
1.3%
69.634280
 
1.2%
81.794177
 
1.2%
59.863929
 
1.1%
131.813525
 
1.0%
146.723435
 
1.0%
126.623322
 
0.9%
116.743118
 
0.9%
87.073114
 
0.9%
92.573089
 
0.9%
Other values (318)323581
89.8%
ValueCountFrequency (%)
50.791242
0.3%
511449
0.4%
51.122473
0.7%
51.16322
 
0.1%
51.27748
 
0.2%
51.47781
 
0.2%
52.061693
0.5%
52.42228
 
0.1%
52.77459
 
0.1%
52.971055
0.3%
ValueCountFrequency (%)
149.75848
 
0.2%
149.082104
0.6%
148.87965
 
0.3%
148.622014
0.6%
147.822158
0.6%
147.35790
 
0.2%
147.181000
 
0.3%
147.171279
 
0.4%
146.723435
1.0%
146.41800
 
0.2%

Interactions

2025-11-14T14:28:12.232837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.122912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.813191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.357073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.913531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.477716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.061090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.643306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.298701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.186968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.878458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.427078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.980150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.546070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.135324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.717209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.368006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.253247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.943957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.498618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.047498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.616037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.207371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.789837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.437334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.318621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.009063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.563920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.113923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.687931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.277279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.861580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.505299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.385038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.077907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.631242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.179197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.759650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.347948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.936209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.573060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.455851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.147789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.702458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.254639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.835491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.420081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.011662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.640611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.675431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.217599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.772244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.323560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.911683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.493892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.085059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.708805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:08.747613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.289415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:09.847667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.410298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:10.988925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:11.572265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T14:28:12.159041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-14T14:28:14.822346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_cars_at home(approx).1coffee_barcostfloristgross_weightlow_fatnum_children_at_homeprepared_foodrecyclable_packagesalad_barstore_sales(in millions)store_sqfttotal_childrenunit_sales(in millions)units_per_casevideo_store
avg_cars_at home(approx).11.0000.0260.0490.0260.0050.0050.0910.0370.0030.0370.0060.0590.0670.0150.0040.030
coffee_bar0.0261.0000.2100.5540.0000.0020.0380.4810.0040.4810.0440.5700.0270.1280.0030.544
cost0.0490.2101.0000.175-0.0000.004-0.0030.1970.0030.197-0.013-0.056-0.008-0.0240.0000.180
florist0.0260.5540.1751.0000.0000.0050.0370.5990.0050.5990.0510.7800.0290.1450.0040.615
gross_weight0.0050.000-0.0000.0001.0000.0980.0000.0040.0980.0040.042-0.000-0.001-0.000-0.0150.000
low_fat0.0050.0020.0040.0050.0981.0000.0000.0050.0300.0050.0240.0030.0030.0060.0840.002
num_children_at_home0.0910.038-0.0030.0370.0000.0001.0000.0480.0060.0480.0160.0090.2760.029-0.0050.045
prepared_food0.0370.4810.1970.5990.0040.0050.0481.0000.0041.0000.0500.6400.0420.1460.0000.614
recyclable_package0.0030.0040.0030.0050.0980.0300.0060.0041.0000.0040.0410.0040.0030.0020.0560.004
salad_bar0.0370.4810.1970.5990.0040.0050.0481.0000.0041.0000.0500.6400.0420.1460.0000.614
store_sales(in millions)0.0060.044-0.0130.0510.0420.0240.0160.0500.0410.0501.0000.0210.0590.449-0.0130.032
store_sqft0.0590.570-0.0560.780-0.0000.0030.0090.6400.0040.6400.0211.000-0.0090.0340.0020.695
total_children0.0670.027-0.0080.029-0.0010.0030.2760.0420.0030.0420.059-0.0091.0000.110-0.0000.039
unit_sales(in millions)0.0150.128-0.0240.145-0.0000.0060.0290.1460.0020.1460.4490.0340.1101.0000.0000.091
units_per_case0.0040.0030.0000.004-0.0150.084-0.0050.0000.0560.000-0.0130.002-0.0000.0001.0000.000
video_store0.0300.5440.1800.6150.0000.0020.0450.6140.0040.6140.0320.6950.0390.0910.0001.000

Missing values

2025-11-14T14:28:12.780539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-14T14:28:12.962546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

store_sales(in millions)unit_sales(in millions)total_childrennum_children_at_homeavg_cars_at home(approx).1gross_weightrecyclable_packagelow_fatunits_per_casestore_sqftcoffee_barvideo_storesalad_barprepared_foodfloristcost
id
08.613.02.02.02.010.301.00.032.036509.00.00.00.00.00.062.09
15.002.04.00.03.06.661.00.01.028206.01.00.00.00.00.0121.80
214.084.00.00.03.021.301.00.026.021215.01.00.00.00.00.083.51
34.023.05.00.00.014.800.01.036.021215.01.00.00.00.00.066.78
42.133.05.00.03.017.001.01.020.027694.01.01.01.01.01.0111.51
59.084.05.05.03.07.260.01.05.033858.01.00.01.01.01.0142.58
64.802.01.00.02.09.580.00.06.027694.01.01.01.01.01.069.47
74.293.02.00.02.016.901.00.02.023688.01.01.01.01.01.068.84
88.553.05.00.02.013.801.00.06.038382.00.00.00.00.00.087.07
93.084.01.00.03.015.701.01.09.027694.01.01.01.01.01.080.29
store_sales(in millions)unit_sales(in millions)total_childrennum_children_at_homeavg_cars_at home(approx).1gross_weightrecyclable_packagelow_fatunits_per_casestore_sqftcoffee_barvideo_storesalad_barprepared_foodfloristcost
id
3603268.043.04.04.02.012.801.01.017.023688.01.01.01.01.01.067.11
3603275.062.03.00.01.018.701.00.015.022478.01.00.00.00.00.0107.20
3603281.262.02.00.04.019.100.00.010.021215.01.00.00.00.00.091.28
3603292.642.03.03.04.018.601.00.028.023688.01.00.01.01.01.085.88
3603302.882.05.00.02.08.250.01.022.023593.00.00.00.00.00.0119.26
3603317.604.05.05.03.013.501.00.033.030268.00.00.00.00.00.0133.42
36033214.444.04.00.04.018.801.01.018.020319.00.00.00.00.00.081.85
36033310.743.00.00.02.011.301.00.035.030584.01.01.01.01.01.087.07
36033411.043.01.00.03.010.200.01.014.030584.01.01.01.01.01.0146.72
3603355.302.02.00.02.010.800.00.021.033858.01.00.01.01.01.0122.47